Table of Contents

Overview

XXX from the XXX Research Team is looking to engage in consultation with the Telethon Kids Institute’s Biometrics team to undertake statistical analysis to determine brief project summary.

Project roles:

Study Overview

Study overview to put the analysis in context.

Research Question

Research Question

Inclusion Criteria

  • Age < 18 years old
  • Scores > 20
  • …

Data Variables Overview

Key Variables

  • Outcome variables (to be modelled separately)
    • XXX
    • XXX

Primary Independent Variable

  • XXX

Adjusting Covariates

  • XXX
  • XXX
  • XXX

Dataset Overview

  • file.xlsx (DD MMM YYYY, provided by XXX)
  • file.sav (DD MMM YYYY, provided by XXX)

Preliminary Data Cleaning Steps

  • XXX
  • XXX

Cohort Summary

There were XXX children born from XXX women over the study study interval, XXX were males that were diagnosed with XXX.

Pairs Plot

Actions

  • XXX to provide cleaned/recoded data to XXX
  • XXX could not be found in the provided data, could XXX please provide some insight

Analysis Plan

  • Research Question 1
    • Linear regression lm() (R Core Team 2019)
      • y ∼ mx + b
      • where, XXX
  • Research Question 2
    • Linear regression glm() (R Core Team 2019)
      • y ∼ mx + b
      • where, XXX

Statistical models will be prepared for the dependant measures identified above with some commentary regarding their interpretation and statistical significance in terms of 95% confidence intervals. Where appropriate, figures will be prepared to help convey the analysis findings.

Commentary around the methods and results of model creation will be provided in the form of a report with bullet-point summaries of the analysis procedures and results. The analysis and reporting will be completed in the R programming language and all R script files associated with the analysis will be made available to the researcher upon request.

Analysis Estimate

The Telethon Clinical Research Centre (TCRC) provides 7.5 hours of biostatistical support. A TCRC biostatistician will carry out analysis for this project and will notify the project sponsor when the analysis reaches 7.5 hours; any further analysis, should it be required, will be completed on a cost-recovery basis at a rate of $875 per day, excluding GST.

Summary of Findings

Tables

Iris Data
(Intercept) 2.25 ***
CI [1.52, 2.98]   
Sepal.Width 0.80 ***
CI [0.59, 1.01]   
Speciesversicolor 1.46 ***
CI [1.24, 1.68]   
Speciesvirginica 1.95 ***
CI [1.75, 2.14]   
N 150       
R2 0.73    
*** p < 0.001; ** p < 0.01; * p < 0.05.

Figures

Raw Data

Model Outcomes

Notes

  • Linear regression (R Core Team 2019)
    • y ∼ mx + b
  • The reported effect estimates have not been exponentiated
  • Effect estimates, 95% confidence intervals that include 0 indicated there was insufficient evidence to establish statistical significance
  • Key observations…
    • 0.80 (95% CI: 0.59 to 1.01)

Conclusion

One or 2 sentences to summarise the analysis.

End Matter

Reproducible Research Information

This document was prepared using the software R (R Core Team 2019), via the RStudio IDE (RStudio Team 2016), and was written in RMarkdown (Allaire et al. 2019).

sessionInfo()
## R version 3.6.1 (2019-07-05)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 8.1 x64 (build 9600)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=English_Australia.1252  LC_CTYPE=English_Australia.1252   
## [3] LC_MONETARY=English_Australia.1252 LC_NUMERIC=C                      
## [5] LC_TIME=English_Australia.1252    
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] rmarkdown_1.16        captioner_2.2.3       jtools_2.0.1         
##  [4] GGally_1.4.0          broom_0.5.2           kableExtra_1.1.0     
##  [7] knitr_1.25            biometrics_1.0.4-999  ProjectTemplate_0.9.0
## [10] lubridate_1.7.4       forcats_0.4.0         stringr_1.4.0        
## [13] tibble_2.1.3          purrr_0.3.2           readr_1.3.1          
## [16] tidyr_1.0.0           dplyr_0.8.3           ggplot2_3.2.1        
## [19] repmis_0.5           
## 
## loaded via a namespace (and not attached):
##  [1] ggstance_0.3.3     tidyselect_0.2.5   xfun_0.9          
##  [4] reshape2_1.4.3     pander_0.6.3       lattice_0.20-38   
##  [7] colorspace_1.4-1   vctrs_0.2.0        generics_0.0.2    
## [10] viridisLite_0.3.0  htmltools_0.3.6    yaml_2.2.0        
## [13] rlang_0.4.0        huxtable_4.6.1     R.oo_1.22.0       
## [16] pillar_1.4.2       glue_1.3.1         withr_2.1.2       
## [19] R.utils_2.9.0      RColorBrewer_1.1-2 R.cache_0.13.0    
## [22] lifecycle_0.1.0    plyr_1.8.4         munsell_0.5.0     
## [25] gtable_0.3.0       rvest_0.3.4        R.methodsS3_1.7.1 
## [28] evaluate_0.14      labeling_0.3       Rcpp_1.0.2        
## [31] scales_1.0.0       backports_1.1.5    webshot_0.5.1     
## [34] hms_0.5.1          digest_0.6.21      stringi_1.4.3     
## [37] grid_3.6.1         tools_3.6.1        magrittr_1.5      
## [40] lazyeval_0.2.2     crayon_1.3.4       pkgconfig_2.0.3   
## [43] zeallot_0.1.0      xml2_1.2.2         data.table_1.12.2 
## [46] reshape_0.8.8      assertthat_0.2.1   httr_1.4.1        
## [49] rstudioapi_0.10    R6_2.4.0           igraph_1.2.4.1    
## [52] nlme_3.1-141       compiler_3.6.1

References

Allaire, JJ, Yihui Xie, Jonathan McPherson, Javier Luraschi, Kevin Ushey, Aron Atkins, Hadley Wickham, Joe Cheng, Winston Chang, and Richard Iannone. 2019. Rmarkdown: Dynamic Documents for R. https://CRAN.R-project.org/package=rmarkdown.

R Core Team. 2019. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

RStudio Team. 2016. RStudio: Integrated Development Environment for R. Boston, MA: RStudio, Inc. http://www.rstudio.com/.